Elgold: gold standard, multi-genre dataset for named entity recognition and linking

Description

The dataset contains 276 multi-genre texts with marked named entities, which are linked to corresponding Wikipedia articles if available. Each entity was manually verified by at least three people, which makes the dataset a high-quality gold standard for the evaluation of named entity recognition and linking algorithms.

Each marked entity in the dataset is assigned to one of the following classes:
EVENT - Named hurricanes, battles, wars, sports events, etc.
FAC - Buildings, airports, highways, bridges, etc.
GPE - Countries, cities, states
LANGUAGE - Any named language
LAW - Named documents made into laws.
LOC - Non-GPE locations, mountain ranges, bodies of water
NORP - Nationalities or religious or political groups
ORG - Companies, agencies, institutions, etc.
PERSON - People, including fictional
PRODUCT - Objects, vehicles, foods, etc. (not services)
WORK_OF_ART - Titles of books, songs, etc.
DISEASE - Names of diseases
SUBSTANCE - Natural substances
SPECIE - Species names of animals, plants, viruses, etc.

The marked entities are embedded directly in the textual files using the following syntax:

The "mention content" is a fragment of the textual file that was marked, "entity class" is the named entity class, and "Wikipedia target" is the normalized name of the English Wikipedia page describing the entity. If the entity cannot be linked sensibly to any article the target is empty but the second pipe (|) is preserved.

There is a guarantee that the double braces in the texts exist only as marked entity syntax. It allows to process the files using simple regular expression: {{[^{}]*}}

Dataset file

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269.2 kB, S3 ETag f9c9f8ab8d9508445811d5692ec0859e-1, downloads: 4

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hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5

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CC BY

Attribution

Details

Year of publication:

2024

Verification date:

2024-01-22

Creation date:

2023

Dataset language:

English

Fields of science:

information and communication technology (Engineering and Technology)

DOI:

10.34808/9wvq-th71

Verified by:

Gdańsk University of Technology

Keywords

References

dataset Elgold partial: News
dataset Elgold partial: Job offers

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